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Swarm Intelligence Techniques and Their
Adaptive Nature with Applications
Anupam Biswas and Bhaskar Biswas
Abstract Swarm based techniques have huge application domain covering multiple
disciplines, which include power system, fuzzy system, forecasting, bio-medicine,
sociological analysis, image processing, sound processing, signal processing, data
analysis, process modeling, process controlling etc. In last two decades numerous
techniques and their variations have been developed. Despite many variations are
being carried out, main skeleton of these techniques remain same. With diverse
application domains, most of these techniques have been modi
fit into a
particular application. These changes undergo mostly in perspective of encoding
scheme, parameter tuning and search strategy. Sources of real world problems are
different, but their nature sometimes found similar to other problems. Hence, swarm
based techniques utilized for one of these problems can be applied to others as well.
As sources of these problems are different, applicability of such techniques are very
much dependent on the problem. Same encoding scheme may not be suitable for the
other similar kind of problems, which has led to development of problem speci
ed to
c
encoding schemes. Sometimes found that, even though encoding scheme is com-
patible to a problem, parameters used in the technique does not utilized in favor of
the problem. So, parameter tuning approaches are incorporated into the swarm based
techniques. Similarly, search strategy utilized in swarm based techniques are also
vary with the application domain. In this chapter we will study those problem
speci
nd
pros and cons of such adaptation. Our study also aims to draw some aspects of such
problem speci
c adaptive nature of swarm based techniques. Essence of this study is to
c variations through which it can be predicted that what kind of
adaptation is more convenient for any real world problem.
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